import torch
import torch.nn as nn
from typing import Dict, Optional
from ..core import ModelCompressor

@ModelCompressor.register_compressor('mixed_precision_backup')
class MixedPrecisionCompressorBackup(ModelCompressor):
    """Simplified mixed precision training compressor backup implementation.
    
    This version uses PyTorch native AMP and provides basic functionality:
    - Mixed precision training
    - Layer-specific precision control
    - Dynamic loss scaling
    - Basic performance metrics
    - Model saving/loading
    
    Compared to the main implementation, this version:
    - Doesn't support distributed training
    - Doesn't include automatic precision tuning
    - Uses PyTorch native AMP instead of APEX
    - Has simpler performance tracking
    """

    def __init__(self, model: nn.Module, config):
        """Initialize with basic mixed precision settings.
        
        Args:
            model: Model to optimize
            config: Configuration dict with:
                - enabled: bool (whether mixed precision is enabled)
                - opt_level: str (ignored in this implementation)
                - loss_scale: str or float ('dynamic' or float value)
                - layer_precision: dict (layer_name: precision)
                - grad_clip: float (gradient clipping value)
        """
        super().__init__(model, config)
        self.enabled = getattr(config, 'enabled', True)
        self.loss_scale = getattr(config, 'loss_scale', 'dynamic')
        self.layer_precision = getattr(config, 'layer_precision', {})
        self.grad_clip = getattr(config, 'grad_clip', None)
        
        # Initialize metrics
        self.metrics = {
            'mixed_precision': self.enabled,
            'loss_scale': str(self.loss_scale),
            'memory_before': self._get_memory_usage(),
            'speedup': 1.0
        }
        
        # Initialize gradient scaler for dynamic loss scaling
        self.scaler = None
        if self.enabled and self.loss_scale == 'dynamic':
            self.scaler = torch.cuda.amp.GradScaler()
        
        # Apply layer precision settings
        if self.enabled and self.layer_precision:
            self._apply_layer_precision()

    def _apply_layer_precision(self):
        """Apply layer-specific precision settings."""
        for name, module in self.model.named_modules():
            if name in self.layer_precision:
                precision = self.layer_precision[name]
                if precision == 'fp16':
                    module.half()
                elif precision == 'fp32':
                    module.float()
                elif precision == 'bf16':
                    module.bfloat16()
                else:
                    raise ValueError(f"Unsupported precision: {precision}")

    def _get_memory_usage(self) -> float:
        """Get current GPU memory usage in MB."""
        if torch.cuda.is_available():
            return torch.cuda.memory_allocated() / (1024 * 1024)
        return 0.0

    def compress(self, train_loader=None) -> nn.Module:
        """Enable mixed precision training.
        
        Args:
            train_loader: Ignored in this implementation
            
        Returns:
            Model prepared for mixed precision training.
        """
        if not self.enabled:
            return self.model
            
        # Record memory after compression
        self.metrics['memory_after'] = self._get_memory_usage()
        if 'memory_before' in self.metrics and 'memory_after' in self.metrics:
            self.metrics['memory_saved'] = (
                self.metrics['memory_before'] - self.metrics['memory_after']
            )
            
        return self.model

    def train_step(self, data, target):
        """Perform training step with mixed precision."""
        if not self.enabled:
            return super().train_step(data, target)
            
        # Forward pass with mixed precision
        with torch.cuda.amp.autocast():
            output = self.model(data)
            loss = self.criterion(output, target)
        
        # Backward pass with gradient scaling
        self.optimizer.zero_grad()
        if self.scaler is not None:
            self.scaler.scale(loss).backward()
            if self.grad_clip is not None:
                self.scaler.unscale_(self.optimizer)
                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip)
            self.scaler.step(self.optimizer)
            self.scaler.update()
        else:
            loss.backward()
            if self.grad_clip is not None:
                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip)
            self.optimizer.step()
        
        return loss.item(), output

    def save_model(self, path: str, metadata: dict = None):
        """Save model with precision settings.
        
        Args:
            path: Path to save model
            metadata: Additional metadata to save
        """
        state = {
            'model_state': self.model.state_dict(),
            'precision_settings': self.layer_precision,
            'scaler_state': self.scaler.state_dict() if self.scaler else None,
            'metrics': self.metrics
        }
        
        if metadata:
            state['metadata'] = metadata
            
        torch.save(state, path)

    def load_model(self, path: str):
        """Load model with precision settings.
        
        Args:
            path: Path to saved model
        """
        state = torch.load(path)
        self.model.load_state_dict(state['model_state'])
        
        if 'precision_settings' in state:
            self.layer_precision = state['precision_settings']
            self._apply_layer_precision()
            
        if 'scaler_state' in state and self.scaler:
            self.scaler.load_state_dict(state['scaler_state'])
            
        if 'metrics' in state:
            self.metrics.update(state['metrics'])

    def get_metrics(self) -> Dict[str, Any]:
        """Get basic performance metrics."""
        return self.metrics